ABSTRACT
Recently, cross-domain recommendation (CDR) has been widely studied in both research and industry since it can alleviate a long-standing challenge of traditional recommendation methods, i.e., data sparsity issue, by transferring the information from a relatively richer domain (termed source domain) to a sparser domain (termed target domain). To our best knowledge, most (if not all) existing CDR methods focus on transferring either the similar content information or the user preferences embedding from the source domain to the target domain. However, they fail to improve the recommendation performance in real-world recommendation scenarios where the items in the source domain are totally different from those in the target domain in terms of attributes. To solve the above issues, we analyzed the historical interactions of users from different domains in the WeChat platform, and found that if two users have similar interests (interactions) in one domain, they are very likely to have similar interests in another domain even though the items of these two domains are totally different in terms of attributes. Based on this observation, in this paper, we propose a novel model named Dual Interests-Aligned Graph Auto-Encoders (DIAGAE) by utilizing the inter-domain interest alignment of users. Besides, our proposed model DIAGAE also leverages graph decoding objectives to align intra-domain user interests, which makes the representation of two users who have similar interests in a single domain closer. Comprehensive experimental results demonstrate that our model DIAGAE outperforms state-of-the-art methods on both public benchmark datasets and online A/B tests in WeChat live-stream recommendation scenario. Our model DIAGAE now serves the major online traffic in WeChat live-streaming recommendation scenario.
- Rianne van den Berg, Thomas N Kipf, and Max Welling. 2017. Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263 (2017).Google Scholar
- Jiangxia Cao, Xixun Lin, Xin Cong, Jing Ya, Tingwen Liu, and Bin Wang. 2022. Disencdr: Learning disentangled representations for cross-domain recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 267--277.Google ScholarDigital Library
- M. Chen, A. Beutel, P. Covington, S. Jain, F. Belletti, and E. H Chi. 2019. Top-k off-policy correction for a REINFORCE recommender system. In WSDM. 456--464.Google Scholar
- Xu Chen, Ya Zhang, Ivor W Tsang, Yuangang Pan, and Jingchao Su. 2020. Towards equivalent transformation of user preferences in cross domain recommendation. ACM Transactions on Information Systems (TOIS) (2020).Google Scholar
- Zhihua Cui, Xianghua Xu, XUE Fei, Xingjuan Cai, Yang Cao, Wensheng Zhang, and Jinjun Chen. 2020. Personalized recommendation system based on collaborative filtering for IoT scenarios. IEEE Transactions on Services Computing, Vol. 13, 4 (2020), 685--695.Google ScholarCross Ref
- Jingtao Ding, Yuhan Quan, Xiangnan He, Yong Li, and Depeng Jin. 2019. Reinforced Negative Sampling for Recommendation with Exposure Data.. In IJCAI. Macao, 2230--2236.Google Scholar
- Ignacio Fernández-Tob'ias and Iván Cantador. 2014. Exploiting Social Tags in Matrix Factorization Models for Cross-domain Collaborative Filtering.. In CBRecSys@ RecSys. Citeseer, 34--41.Google Scholar
- Wenjing Fu, Zhaohui Peng, Senzhang Wang, Yang Xu, and Jin Li. 2019. Deeply fusing reviews and contents for cold start users in cross-domain recommendation systems. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 94--101.Google ScholarDigital Library
- Chen Gao, Xiangning Chen, Fuli Feng, Kai Zhao, Xiangnan He, Yong Li, and Depeng Jin. 2019. Cross-domain recommendation without sharing user-relevant data. In The world wide web conference. 491--502.Google Scholar
- Chen Gao, Yu Zheng, Nian Li, Yinfeng Li, Yingrong Qin, Jinghua Piao, Yuhan Quan, Jianxin Chang, Depeng Jin, Xiangnan He, et al. 2023. A survey of graph neural networks for recommender systems: challenges, methods, and directions. ACM Transactions on Recommender Systems, Vol. 1, 1 (2023), 1--51.Google ScholarDigital Library
- Yong Gao, Huifeng Guo, Dandan Lin, Yingxue Zhang, Ruiming Tang, and Xiuqiang He. 2021. Content Filtering Enriched GNN Framework for News Recommendation. arXiv preprint arXiv:2110.12681 (2021).Google Scholar
- Jonathan L Herlocker, Joseph A Konstan, Loren G Terveen, and John T Riedl. 2004. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS), Vol. 22, 1 (2004), 5--53.Google ScholarDigital Library
- Guangneng Hu, Yu Zhang, and Qiang Yang. 2018. Conet: Collaborative cross networks for cross-domain recommendation. In Proceedings of the 27th ACM international conference on information and knowledge management. 667--676.Google ScholarDigital Library
- Rong Hu and Pearl Pu. 2011. Enhancing collaborative filtering systems with personality information. In Proceedings of the fifth ACM conference on Recommender systems. 197--204.Google ScholarDigital Library
- SeongKu Kang, Junyoung Hwang, Dongha Lee, and Hwanjo Yu. 2019. Semi-supervised learning for cross-domain recommendation to cold-start users. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 1563--1572.Google ScholarDigital Library
- Pan Li and Alexander Tuzhilin. 2020. Ddtcdr: Deep dual transfer cross domain recommendation. In Proceedings of the 13th International Conference on Web Search and Data Mining. 331--339.Google ScholarDigital Library
- Dandan Lin, Shijie Sun, Jingtao Ding, Xuehan Ke, Hao Gu, Xing Huang, Chonggang Song, Xuri Zhang, Lingling Yi, Jie Wen, et al. 2022. PlatoGL: Effective and Scalable Deep Graph Learning System for Graph-enhanced Real-Time Recommendation. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 3302--3311.Google ScholarDigital Library
- Meng Liu, Jianjun Li, Guohui Li, and Peng Pan. 2020. Cross domain recommendation via bi-directional transfer graph collaborative filtering networks. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 885--894.Google ScholarDigital Library
- Jorge M Lobo, Alberto Jiménez-Valverde, and Raimundo Real. 2008. AUC: a misleading measure of the performance of predictive distribution models. Global ecology and Biogeography, Vol. 17, 2 (2008), 145--151.Google Scholar
- Tong Man, Huawei Shen, Xiaolong Jin, and Xueqi Cheng. 2017. Cross-domain recommendation: An embedding and mapping approach.. In IJCAI, Vol. 17. 2464--2470.Google Scholar
- Yuhan Quan, Jingtao Ding, Chen Gao, Lingling Yi, Depeng Jin, and Yong Li. 2023. Robust Preference-Guided Denoising for Graph based Social Recommendation. In Proceedings of the ACM Web Conference 2023. 1097--1108.Google ScholarDigital Library
- Chuan Shi, Binbin Hu, Wayne Xin Zhao, and S Yu Philip. 2018. Heterogeneous information network embedding for recommendation. IEEE Transactions on Knowledge and Data Engineering, Vol. 31, 2 (2018), 357--370.Google ScholarDigital Library
- Ajit P Singh and Geoffrey J Gordon. 2008. Relational learning via collective matrix factorization. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. 650--658.Google ScholarDigital Library
- Shulong Tan, Jiajun Bu, Xuzhen Qin, Chun Chen, and Deng Cai. 2014. Cross domain recommendation based on multi-type media fusion. Neurocomputing, Vol. 127 (2014), 124--134.Google ScholarDigital Library
- Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research, Vol. 9, 11 (2008).Google Scholar
- Petar Velivc ković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017).Google Scholar
- Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019a. Neural graph collaborative filtering. In Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval. 165--174.Google ScholarDigital Library
- Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, and Philip S Yu. 2019b. Heterogeneous graph attention network. In The World Wide Web Conference. 2022--2032.Google ScholarDigital Library
- Yifan Wang, Weizhi Ma, Min Zhang, Yiqun Liu, and Shaoping Ma. 2023. A survey on the fairness of recommender systems. ACM Transactions on Information Systems, Vol. 41, 3 (2023), 1--43.Google ScholarDigital Library
- Yinwei Wei, Xiang Wang, Liqiang Nie, Xiangnan He, Richang Hong, and Tat-Seng Chua. 2019. MMGCN: Multi-modal graph convolution network for personalized recommendation of micro-video. In Proceedings of the 27th ACM international conference on multimedia. 1437--1445.Google ScholarDigital Library
- Hong-Jian Xue, Xinyu Dai, Jianbing Zhang, Shujian Huang, and Jiajun Chen. 2017. Deep matrix factorization models for recommender systems.. In IJCAI, Vol. 17. Melbourne, Australia, 3203--3209.Google ScholarCross Ref
- J. Yang, X. Yi, D. Cheng, L. Hong, Y. Li, S. Wang, T. Xu, and E. H Chi. 2020. Mixed negative sampling for learning two-tower neural networks in recommendations. In Companion Proceedings of the Web Conference 2020. 441--447.Google ScholarDigital Library
- Feng Yuan, Lina Yao, and Boualem Benatallah. 2019. DARec: Deep Domain Adaptation for Cross-Domain Recommendation via Transferring Rating Patterns. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (Macao, China) (IJCAI'19). AAAI Press, 4227--4233.Google ScholarCross Ref
- Cheng Zhao, Chenliang Li, Rong Xiao, Hongbo Deng, and Aixin Sun. 2020. CATN: Cross-domain recommendation for cold-start users via aspect transfer network. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 229--238.Google ScholarDigital Library
- Junzhou Zhao, John CS Lui, Don Towsley, Xiaohong Guan, and Yadong Zhou. 2011. Empirical analysis of the evolution of follower network: A case study on douban. In 2011 IEEE conference on computer communications workshops (INFOCOM WKSHPS). IEEE, 924--929.Google ScholarCross Ref
- Z. Zhao, L. Hong, L. Wei, J. Chen, A. Nath, S. Andrews, A. Kumthekar, M. Sathiamoorthy, Xinyang Yi, and E. H Chi. 2019. Recommending what video to watch next: a multitask ranking system. In Recsys. 43--51.Google Scholar
- Jiawei Zheng, Hao Gu, Chonggang Song, Dandan Lin, Lingling Yi, and Chuan Chen. 2023. Dual Interests-Aligned Graph Auto-Encoders for Cross-domain Recommendation in WeChat. In https://github.com/csjwzheng/Dual-Interests-Aligned-Graph-Auto-Encoders-for-Cross-domain-Recommendation-in-WeChat.Google Scholar
- Jiawei Zheng, Qianli Ma, Hao Gu, and Zhenjing Zheng. 2021. Multi-view Denoising Graph Auto-Encoders on Heterogeneous Information Networks for Cold-start Recommendation. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2338--2348.Google ScholarDigital Library
- Feng Zhu, Yan Wang, Chaochao Chen, Guanfeng Liu, and Xiaolin Zheng. 2020. A Graphical and Attentional Framework for Dual-Target Cross-Domain Recommendation.. In IJCAI. 3001--3008.Google Scholar
- Feng Zhu, Yan Wang, Chaochao Chen, Jun Zhou, Longfei Li, and Guanfeng Liu. 2021b. Cross-domain recommendation: challenges, progress, and prospects. arXiv preprint arXiv:2103.01696 (2021).Google Scholar
- Qiannan Zhu, Xiaofei Zhou, Zeliang Song, Jianlong Tan, and Li Guo. 2019. Dan: Deep attention neural network for news recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 5973--5980.Google ScholarDigital Library
- Yongchun Zhu, Kaikai Ge, Fuzhen Zhuang, Ruobing Xie, Dongbo Xi, Xu Zhang, Leyu Lin, and Qing He. 2021a. Transfer-meta framework for cross-domain recommendation to cold-start users. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1813--1817.Google ScholarDigital Library
- Yongchun Zhu, Zhenwei Tang, Yudan Liu, Fuzhen Zhuang, Ruobing Xie, Xu Zhang, Leyu Lin, and Qing He. 2022. Personalized transfer of user preferences for cross-domain recommendation. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. 1507--1515.Google ScholarDigital Library
Index Terms
- Dual Interests-Aligned Graph Auto-Encoders for Cross-domain Recommendation in WeChat
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